class_docstr: A compact implementation of agents for Atari 100k.
code: def log_transition(
      self, observation, action, reward, terminal, episode_end
  ):
      self._last_observation = self._observation
      self._record_observation(observation)
  
      if not self.eval_mode:
          self._store_transition(
              self._last_observation,
              action,
              reward,
              terminal,
              episode_end=episode_end,
          )
  
imports: import copy
  import functools
  from absl import logging
  from dopamine.jax import networks
  from dopamine.jax.agents.full_rainbow import full_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import tensorflow
label_desc: Use image node_id 11 for calling the Atari100kRainbowAgent obj's underlying member method code with example usage: obj.log_transition(observation, action, reward, terminal, episode_end) without return types